As is appreciated by those familiar with the art, mismatches of demand and supply are costly to sellers in a competitive market because such mismatches often result in missed sales opportunities, lost profits, excessive expediting costs, lost market share, and poor customer service. To maximize sales and marketing effectiveness, companies must accurately predict future customer demand and use this information to drive their business operations from manufacturing to operations to distribution. This need for accurate predictions of demand is especially important for those involved in e-business due to the ease with which buyers can find alternative sellers that can satisfy their demand.
In the manufacturing and distributing industries, supplying products in response to the current level of customer demand with a minimum of overstocking reduces stocking costs and distribution expenses and thus leads to a reduction of the sales unit price of products. Typically, this also leads to an enhancement of profit margins. It is therefore necessary for sellers to forecast product demand precisely such that they then can determine a sales plan, production plan, and distribution plan according to an accurate forecast of the demand trend of customers.
Conventional methods of forecasting demand by analyzing the trend of past sales results are performed with the goal of the forecaster being to apply the most accurate statistical analysis techniques and econometric modeling to provide the most accurate forecast possible. In these conventional methods, time series forecasting is performed which develops and uses various forecasting algorithms that attempt to describe the knowledge of the business and fluctuation trend of sales results as evidenced by past history in the form of a rule. Developing such forecasting algorithms (as well as computerized systems for utilizing such algorithms) is typically a labor-intensive task.
Unfortunately, however, it is common for product demand trends to change in a short lifecycle. When this is the case, the data used in providing a forecast rapidly becomes old and the precision of forecasting lowers. Therefore, in order to keep a high precision in forecasting, algorithms (and historic data points used to generate those algorithms) must be maintained on an ongoing basis as well as be able to adjust their forecasts relatively easily.
In addition to the difficulty in computing business demand in general is the complexity introduced by different periodic demand patterns in certain industries based upon day of the week, seasons of the year, or other recurring events. For example, in certain industries, such as the restaurant or retail industries, foot traffic, product preferences and sales volume vary according to day of the week. Similarly, predictable seasonal, annual, and/or periodic fluctuations in product demand are present to some extent in many industries. In addition, non-periodic events promotional programs, local events, holidays, and the like, all alter the demand levels faced by a business. An accurate forecast of demand must be able to accommodate these periodic and non-periodic effects on demand level. Unfortunately, the complexity of formulating a single algorithm for doing so accurately has traditionally relegated businesses to making educated guesses regarding what type of demand to expect based upon their experience and records regarding past business demand. As one might expect, as businesses become larger and more complex, making such predictions becomes more problematic and risky. Further, it is unpractical, if not impossible, for a human being to calculate and predict fluctuations in business demand on a more frequent basis (such as hourly or even daily) even though it may be desirable to do so.
Therefore, since one of the more important issues encountered in production planning stems from the uncertainties associated with future demand for products, a vast volume of literature and efforts within the industry has attempted to address this issue. Presently, however, production, materials and transportation planning based upon forecasted demand still presents a significant challenge. While there have been many studies in the demand planning theory area, the advances achieved thus far are either based upon oversimplified assumptions or alternatively are computationally infeasible for real world application. Thus, heretofore there has not been developed a flexible yet substantially automated manner by which sellers in a competitive market can satisfy their demand forecasting needs.
By way of example, U.S. patent application Ser. No. 6,049,742 to Milne et al. discloses a computer software tool for comparing projected supply planning decisions with expected demand profiles. The computer system disclosed by Milne provides routines that compare projected supply with actual experienced demand to help the user of the software tool to configure it to best meet their business's requirements. Unfortunately, like most known demand forecasting methodologies, Milne is inflexible in that it does not help users to develop and identify improved models by comparing multiple alternative models for various products within various markets.
Similarly, U.S. patent application Ser. No. 6,138,103 to Cheng et al. discloses a decision-making method for predicting uncertain demand. The Cheng system uses a matrix to represent potential demand scenarios and their relative probabilities of occurring, and these matrices are then used to calculate a production-planning schedule based upon the most probable outcome of the uncertain demand. Cheng, like Milne, fails to teach manners for developing alternative algorithms for forecasting future demand as well as fine-tuning such demand forecasting algorithms.
Additionally, U.S. Pat. No. 5,459,656 to Fields et al. discloses a system that measures and stores business demand data over a plurality of time intervals and projects the business demand for products in near-future time intervals using percentage-based demand curves. The Fields system allows the creation of a plurality of demand curves for the items to determine near-future demand by using defined functions and variables. Business demand projections for current and near-future time intervals can then be revised in response to variances in actual business demand data as it received. Fields, however, fails to produce forecasts that proactively take into account many factors that impact upon future demand including the variability of individual markets and the impact of product promotions.
Finally, U.S. Pat. No. 6,032,125 to Ando discloses a demand forecasting system that allows users to forecast demand based upon algorithms derived from data of various time periods including recent months, similar periods in previous years, and combinations thereof. The Ando patent, however, fails to take into account that data regarding past demand can come from various sources within a single organization or supply chain (such as sales data, returns, wholesale data, etc.). Further, Ando fails to teach systems or methods for deriving various demand forecasting models and revising those models over the passage of time.
Thus, there remains a need in the art for improved systems and methods that can proactively develop alternative models to predict demand across multiple levels of an organization's supply chain so as to avoid costly mismatches of demand and supply.